Automatic image segmentation methods and apparatus

ABSTRACT

The invention provides methods and apparatus for image processing that perform image segmentation on data sets in two- and/or three-dimensions so as to resolve structures that have the same or similar grey values (and that would otherwise render with the same or similar intensity values) and that, thereby, facilitate visualization and processing of those data sets.

This application claims the benefit of priority of U.S. PatentApplication Ser. No. 60/989,915, filed Nov. 23, 2007, the teachings ofwhich are incorporated herein by reference.

BACKGROUND OF THE INVENTION

The invention pertains to digital data processing and, moreparticularly, to the visualization of image data. It has application, byway of non-limiting example, in medical imaging, microscopy, geophysics,non-destructive testing.

Data sets in diagnostic medical imaging and other disciplines such asmicroscopy, geo-physics, non destructive testing etc., are growing insize and number. Efficient visualization methods are thereforeincreasingly important to enable clinicians, researchers and others toanalyze digital image data. Image segmentation and the extraction ofstructures from images for visualization and analysis can be helpful forthis purpose.

Image segmentation is an automated technique that facilitatesdistinguishing objects and other features in digital images. Thetechnique can be used, for example, to simplify digitized images so thatthey can be more readily interpreted by computers (e.g., image analysissoftware) and/or by their users. Thus, for example, image segmentationcan be used to simplify a digitized x-ray image of a patient who hasconsumed a barium “milkshake.” In its original form, such an image ismade up of pixels containing a wide range of undifferentiated intensityvalues that—although, possibly recognizable to the human eye as skeletalbones and digestive tract—are largely uninterpretable by a computer.

Image segmentation can remedy this by categorizing as being of potentialinterest (e.g., “not background”) all pixels of a selected intensityrange—typically, all intensities above a threshold value. Alternatively,image segmentation can rely on finding all edges or borders in theimage. A related, but still further alternative technique, is toidentify all “connected components”—i.e., groupings of adjacent pixelsin the image of the same, or similar, pixel intensity (or color). Yetanother image segmentation technique involves “region growing,”in whichconnected components are grown around seed point pixels known to residewithin structures of interest.

Continuing the example, threshold-based segmentation can be applied toan x-ray image such that pixels whose intensities are above, say, 200(out of 255) are labelled as barium-containing digestive organs and allother pixels are labelled as background. If the pixel intensities of theformer are uniformly adjusted to a common value of, say, 255, and thepixel intensities of the latter are uniformly adjusted to, say, 0, theresulting “segmented” image, with only two levels of intensity values (0and 255) is often more readily interpreted by man and machine alike.

An object of the invention is to provide improved methods and apparatusfor digital data processing.

A related object is to provide such improved methods and apparatus forthe visualization of image data.

A still further related aspect of the invention is to provide suchmethods and apparatus as can be applied in medical imaging, microscopy,geophysics, non destructive testing, and other imaging applications.

SUMMARY OF THE INVENTION

The invention provides methods and apparatus for image processing thatperform image segmentation on data sets in two- and/or three-dimensionsso as to resolve structures that have the same or similar grey values(and that would otherwise render with the same or similar intensityvalues) and that, thereby, facilitate visualization and processing ofthose data sets.

Such methods and apparatus can be used, for example, to apply differentvisualization parameters or rendering methods to different structures orregions of the data set, including completely hiding one or more thoseregions. In particular, for example, aspects of the invention providemethods and apparatus for medical imaging that automatically extractbone structures in computed tomography (CT) 3D run-off studies withoutremoving vessel structures (although those vessel structures may havethe same or similar grey values). Further aspects of the inventionprovide for automatic removal of these and/or other structures shown in3D (and other dimensional) images generated by other medical imagingtechniques.

Thus, in one aspect, the invention provides a method for processing oneor more two-dimensional (2D) image slices of a three-dimensional (3D)volume. The method includes the steps of identifying a region of the 3Dvolume to which the 2D image belongs, and performing segmentation on the2D image to identify connected components. The method further calls forlabeling pixels of those connected components based on geometriccharacteristics (e.g., shape and/or size), where such labeling is basedon a volumetric region to which the 2D image belongs. By way of example,methods of the invention can be applied to processing 2D image “slices”generated via computed tomography, e.g., in medical imaging. For thesepurposes, the “region” is a region of the patient's body.

Related aspects of the invention provide such methods wherein the stepof identifying the region to which the 2D image belongs includescomputing a histogram of the image and comparing it with one or moreother histograms, e.g., from an atlas of average histograms previouslydetermined for the 3D volume (such as, for example, an atlas of averageCT histograms for the human body). The image can be assigned, forexample, to the region of the 3D volume associated with thebest-matching histogram from the atlas. In instances where multiple 2Dimages are assigned to regions, the method can include checking thoseassignments for consistency and re-assigning one or more 2D images toanother region, as necessary.

Still further related aspects of the invention provide such methodswherein the step of identifying the region to which the 2D image belongsincludes determining additional information about the 2D image byperforming threshold segmentation and determining therefrom any of (a) anumber of connected components in the resulting image and/or (b) a totalarea occupied by selected structures and/or types of structures (e.g.,body tissues). Alternatively, or in addition, the step of determiningadditional information can include computing a histogram of selectedstructures (e.g., a histogram of a region within the body and/or aregion covered by body tissues).

Other aspects of the invention provide methods as described above inwhich the step of performing segmentation on the 2D image includesperforming a threshold segmentation to label identically pixelsrepresenting the structures of interest. When such methods are appliedto processing CT images, for example, such structures may be vasculature(e.g., blood vessels) and bone.

Such methods can, according to related aspects of the invention, includeidentifying connected components in the 2D image, following thresholdsegmentation, and relabeling pixels as differing types of structures ofinterest (e.g., vasculature versus bone, and vice versa) based ongeometric properties of those connected components, e.g., in the contextof the (body) region to which the 2D image has been assigned.

Still further related aspects of the invention provide such methods inwhich the step of performing segmentation on the 2D image to identifyand label connected components based on their geometric characteristicsincludes performing such segmentation with increasingly largerthresholds to identify connected components that separate, as a result,into multiple components. When this is detected, the method calls forrelabeling as differing types of structures of interest (again, by wayof example, vasculature versus bone, and vice versa) making up thoseseparated components.

Still further aspects of the invention provide methods as describedabove that include performing segmentation on a 3D data set formed fromthe plurality of the 2D images so as to label voxels representing thestructures of interest (e.g., vasculature versus bone). According tothese aspects of the invention, such segmentation can includeflood-filling the 3D data set from one or more seed points placed atvoxels in such structures of interest.

Flood-filling can proceed with increasing voxel intensity ranges until aconnected component or region created thereby overextends. In relatedaspects of the invention, flood-filling is terminated when a connectedcomponent formed thereby either (i) stair-steps, or (ii) overruns a seedpoint placed in a voxel representing another structure of interest.

According to still further related aspects of the invention, the methodcalls for using model-based detection to find structures of interest inthe 3D data set. And, yet, in still further related aspects of theinvention, pixels corresponding to voxels labeled using such 3Dsegmentation are not relabeled if and when 2D segmentation of the typedescribed above is performed.

These and other aspects of the invention are evident in the drawings andthe description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the invention may be attained byreference to the drawings, in which:

FIG. 1A depicts a digital data processing environment of the type inwhich the invention is practiced;

FIG. 1B overviews a method according to the invention;

FIG. 2 depicts a result of segmentation of a data set of imagesutilizing two-dimensional and/or three-dimensional segmentation-basedmethods according to the invention;

FIG. 3 depicts a method according to the invention for assigning a 2Dimage slice to a volumetric region;

FIG. 4 depicts a method according to the invention for 2D segmentation;

FIG. 5 depicts a method according to the invention for 3D segmentation;

FIGS. 6-8 depict a melding and segregation of structures in an imagesubject to 2D segmentation according to the invention.

DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENT Overview

Described below are methods and apparatus for image processing accordingto the invention. These may be realized on workstations, personalcomputers, and other digital data processing systems of the type knownin the art and used for image processing and/or reconstruction, anexample of which is provided in FIG. 1A.

The system 10 includes a central processing unit (CPU) 30, dynamicmemory (RAM) 32, and I/O section 34, all of the type known in the art,as adapted in accord with the teachings hereof. The digital dataprocessor 26 may be coupled, via I/O section 34, with an image source(not shown), as well as with a monitor or other presentation device 28on which images processed by system 10 may be displayed. I/O section 34can also provide coupling to other apparatus (e.g., storage devices)and/or networks to which processed images can be transmitted forstorage, displayed or otherwise. Illustrated digital data processor 26may also include one or more graphical processing units (GPUs) 36, orother hardware acceleration devices, to facilitate execution of themethods described herein, though, such is not a requirement of theinvention. Moreover, although digital data processor 26 is shown herehas a stand-alone device, in other embodiments, it may be arrangedotherwise, e.g., as an embedded processor.

The image source generates or otherwise supplies a plurality of images14 for segmentation. In the illustrated embodiment, these represent,individually, two-dimensional (2D) “slices” of a three-dimensional (3D)volume. Indeed, in the example discussed below, they represent axialslices of a computed tomography (CT) scan of a patient's body, orportion thereof. More generally, however, images supplied by source 12may derive from any number of technical pursuits, medical andnon-medical alike, such as, by way of non-limiting example, microscopy,geophysics, non destructive testing, and so forth. Furthermore, althoughreferred to as such in the illustrated embodiment, in other embodiments“images” 14 may comprise two-dimensional slices of data of any naturethat make up the 3D volume.

FIG. 1B overviews operation of the system 10 and, more particularly, ofcomputer software 40 executing thereon for purposes of segmenting images14. Although such methods according to the invention are implemented incomputer software (or, a “computer program”) in other embodiments, suchapparatus and/or methods may be implemented solely in hardware, infirmware or otherwise.

Referring to the drawing, the system 10 and, more particularly, thesoftware 40 executes the following operations:

-   -   First the image data is loaded into main memory 32 of the        computer 26. See, step 42. This can be effected all at once or        in stages, e.g., as image data from the respective slices is        needed for processing. In the illustrated embodiment, that image        data comprises the plurality of 2D images 14 that make up a 3D        volume (hence, the image data is referred to as a “3D data        set”). In other embodiments, processing is performed on only a        single individual 2D image.    -   Then, specific structures represented in the images are        segmented, i.e., pixels (and voxels) belonging to those        structures are marked (or “labeled”) in a suitable data        structure in memory 32. See, step 44. In instances where the        images 14 are from medical imaging apparatus, the structures can        be, for example, anatomical structures. The data structure may        comprise any collection of data elements (e.g., an array,        struct, linked list, etc.) of the type known in the art (as        adopted for use herewith) suitable for holding labels that        designate structures to which the pixels and/or voxels belong.    -   Then the processed image data is displayed, e.g., on output        device 28, using different rendering settings for the different        structures. See step 46. In other embodiments, the processed        image data may be subjected to still further processing on        device 26, before, after, or in lieu of display. Alternatively,        or in addition, it may be transmitted elsewhere (e.g., to other        equipment and/or locations) for storage, processing, and/or        display.

FIGS. 3-5 depict further details of the processing effected in step 44.The discussion of those figures below uses, as an example, segmentationof bone and blood vessels in the images 14, e.g., to enable the formerto be removed during display of the segmented images in step 46.However, as noted above, it will be appreciated that the invention canbe used for a variety of other purposes and in a variety of otherapplications.

To understand the example, it will be appreciated that, often, CTstudies use contrast agent to visualize the blood vessels. In such casesthe blood vessels appear bright in the CT images, similar to bonestructures. When rendering the image in maximum intensity projectionmode (MIP), then the bright bone structures will often occlude thevessel structures which are typically much thinner than bone. Howeversince both contrasted vessels and bone have the same or similarintensity values, standard rendering methods to distinguish them, suchas assigning different transparency values based on intensity, willfail. Apparatus and methods according to the invention resolve this,allowing for the segmentation of structures represented in images14—i.e., structures that would otherwise render with the same or similarintensity values—so that they may be rendered (and/or processed)differently from one another.

In the example that follows, those methods are applied to segment pixelsrepresenting bone differently from those representing blood vessels sothat they can be rendered differently and, particularly, so that thebone can be rendered invisibly in step 46. FIG. 2 illustrates the resultof executing such a method. In the left hand half of the figure amaximum intensity projection (MIP) of a CT runoff study is shown. Mostof the vascular structure is hidden by the bone. In the right hand halfof the figure voxels belonging to bone were segmented using methods ofthe invention and then left out in the rendering, thus, revealing thevessel structure. Note that in the discussion that follows, the images14 are referred to, individually, as “slices,” “2D slices,” or the likeand, collectively, as the “data set,” “3D data set,” or the like.

Identifying Body Regions

Referring to FIG. 3, the illustrated method first identifies volumetricregions to which the respective image slices 14 belong. In the example,these are body regions, i.e., ranges of successive slices correspondingto certain anatomy, such as foot, lower leg, knee, upper leg, hip, lowerabdomen, lung. This is done as follows:

A histogram is computed for each slice of the data set. See step 48. Thehistogram is computed in the conventional manner known in the art.

The histograms are then used to determine which volumetric regions therespective slices belong to. In the example, the histograms are comparedwith an anatomic atlas containing average histograms for the differentbody regions. See step 50. For each of the slices a correlation such asa cross-correlation is computed between the slice histogram and saidatlas of average histograms in order to determine which body part'shistogram the slice most closely matches and therefore most likelybelongs to. The slice is then assigned to the respective body part. Seestep 52.

Once all (or a subset of all) of the slices have been assigned, then itis checked whether the individual slice assignment is consistent withthe overall volumetric region to which they pertain. In the example, theslice assignments are checked to insure that they are consistent withhuman anatomy, e.g. in the above example, there can only be one range ofslices assigned to each of the body regions such as “lower leg” and thebody regions must be ordered in the correct spatial order. See step 54.

If inconsistencies are detected, slices are re-assigned to differentvolumetric regions—in the example, body regions—to obtain a consistentoverall assignment. In many instances, multiple different sets of slicescould be chosen for re-assignment in order to achieve overallconsistency. Preferably, methods and apparatus according to theinvention choose the one which re-assigns the smallest number of slicespossible to achieve consistency.

As those skilled in the art will appreciate, additional criteria (orparameters) can be utilized to match slices to atlas entries—or, moregenerally, to determine which volumetric regions the respective slicesbelong to. This is illustrated in steps 56-64. Generally speaking, thisinvolves performing a threshold segmentation on each slice and, then,determining (a) the number of connected components in the image and/or(b) the total area occupied by pixels representing structures and/ortypes of structures of interest (in the example, body tissues). It mayalso involve computing a second histogram for the latter, i.e.,structures of interest (e.g., pixels within the body).

More particularly, by way of example, in the illustrated embodimentadditional parameters for processing axial slices of a CT scan, e.g.,for segmentation of bone and blood vessels, can be computed in a mannersuch as the following:

A threshold segmentation is performed in each slice with a threshold of−1000 HU (Hounsfield Units) which separates body tissue from air. Seestep 58. The number of connected components in the threshold segmentedimage is computed, as well as the total area covered by body pixels. Seesteps 60-62. A second histogram (subsequently referred to as BodyHistogram) is computed for each slice which only takes into accountpixels inside the body, i.e., enclosed in a connected component in saidthreshold segmented image. See step 64. It will be appreciated that, insome applications, the threshold used to separate body tissue from airmay vary (e.g., from about −1000 HU to other values) and that, in otherembodiments, still other thresholds are used, e.g., to distinguish amongother structures and/or types of structures of interest.

The additional information determined for each slice following thresholdsegmentation (in step 58)—namely, the number of connected components,the total area occupied by structures of interest (e.g., body tissue),and/or a histogram of those structures—can be used along with (orinstead of) the slice histogram computed in step 48, to determinevolumetric regions (e.g., body regions) to which the respective imageslices 14 belong.

In the example, the additional information is used to assign slices tobody regions and/or anatomical structures using criteria such as, forexample, “slices which have a significant number (>25%) of air pixels(pixels with intensity <−1000 HU) are assigned to lung” or “the numberof connected components in slices in region lower leg is two” or “theknee region is between 2 cm and 20 cm in size”. Adding such criteria canmake the illustrated method and apparatus more robust and/or moreefficient. The exact selection of criteria is application- and image-setdependent: for example a criterion like “the number of connectedcomponents in slices in region lower leg is two” should not be chosen ifone-legged patient data are to be processed.

Performing Segmentation in Slices

Once the slices have been assigned to volumetric regions (in theexample, body regions), 2D segmentation is performed to identifyconnected components of potential interest (bone and vessel) and theirpixels labelled (or categorized) based on geometric characteristics. Inthe example, this differentiates pixels depicting bone from bloodvessels, though, in other applications it may differentiate pixelsdepicting other structures from one another. In that light, it will beappreciated that some of the algorithms discussed below are specific tobone/vessel segmentation and that different algorithms and/or thresholdsmay be employed in different applications. Moreover, even in the contextof bone/vessel segmentation, the algorithms may vary within differentregions of the body as will be described in the following.

In each slice, a threshold segmentation is performed using a firstthreshold. See step 66. A value is chosen so as to result in identicallabeling of pixels representing the structures of interest (in theexample, bones and blood vessels), yet, different labeling forstructures that are not of interest (e.g., organs). In the example, thisis a threshold of 130 HU —though it will be appreciated that differentthreshold values (e.g., those of about 130 HU or otherwise) can be useddepending on the acquisition protocol and user input. As noted, thissegmentation will label both, bone and vessels.

In the next step, connected components in each slice are (re-)labelledbased on their geometries characteristics (e.g., size and/or shape).This labeling is preferably done in the context of the volumetric regionto which the respective slice is assigned. See step 68. Bones normallyhave a greater diameter than vessels. Therefore, in the example, aftersaid threshold segmentation all connected components with an areagreater than the diameter of the largest vessel which can occur in therespective body region is assigned to bone, all others are assigned tovessel. As noted, this may be further qualified by the context of thebody region to which the slice is assigned. Thus, In the leg, except forthe knee region, one can add the additional constraint that only thelargest connected component (upper leg) or the two largest connectedcomponents (lower leg) are to be considered bone. Caution is requiredwhen cases are to be processed in certain applications were pathologiesare present which could violate those latter criteria, such as patientswith bone fractures.

Sometimes the structures that will ultimately be differentiated (in theexample, bones and blood vessels) run very close to one another. In suchcases, in some slices the cross section of one structure (e.g., thebone) and of another structure (e.g., the vessel) near it may actuallyappear melted into one connected component in the threshold segmentationgenerated in step 66. This is illustrated in FIG. 6, which shows part ofan axial slice in the lower leg. The two bones and some vessels can beclearly seen as bright spots. FIG. 7 shows a threshold segmentation witha certain threshold (268 HU). As can be seen, one vessel which runsclose to the upper bone appears to be connected in this segmentation.Therefore if the whole connected component was selected as bone, thenthat piece of the vessel would be segmented incorrectly.

The method used to prevent that is illustrated in steps 70-74 andinvolves performing segmentation with increasingly larger thresholds,checking after each successive segmentation to see if another otherwiseseemingly connected component separates into multiple components and, ifso, relabeling the separated components as different structures.Application of this to the example follows:

Assume that a connected component is identified as bone in one slice. Wewill refer to (and label) the set of pixels comprising this component asB. Now the threshold is increased from the first or originalthreshold—in this case, 130 HU—in steps of 1 HU up to 400 HU. See step70. Of course, it will be appreciated that other step sizes may be used.When increasing the threshold, the segmented bone structure shrinks.Often, in the case of a melted vessel/bone structure, the vessel willseparate in the course of this process. This can be seen in FIGS. 7 and8. While in FIG. 7 the vessel and the upper bone are still connected, inFIG. 8 with a higher threshold, they are separated.

After each increase of the threshold it is checked whether a connectedcomponent previously identified as bone gets separated into multiplecomponents, a large one and one and a small one. See step 72. Once thathappens, the smaller component is labelled as vessel. See step 74. Welabel the set of pixels comprising said smaller component as V and theset of pixels comprising said larger component as B*.

In the original segmentation with the first threshold (here, 130 HU),all pixels belonging to B are now reassigned according to the followingcriterion: Any pixel which is part of B* remains bone. Any pixel whichis part of V is assigned to vessel, i.e. removed from B. Any pixel whichis neither part of B* nor V is re-assigned to vessel if it is closer toV than to B*. The distance of a pixel p to a set of pixels S is definedto be the shortest distance between p and any of the pixels belonging toS.

Performing Segmentation in 3D

In addition to (or in lieu of) performing segmentation on the 2D imageslices 14 as described above, methods and apparatus according to theinvention performs segmentation on 3D data sets formed from those slicesutilizing flood-filling (or region growing). This permits labelingvoxels representing structures of interest (e.g., those representingbone and blood vessels) and, as a consequence, provides further basisfor differentially labeling the corresponding voxels (and pixels).

By way of overview, this 3D segmentation involves placing seed points invoxels which are known to represent structures of interest andflood-filling from those seed points to mark connected componentsbelonging to the same respective structures. The voxel intensity rangefor the flood-fill is gradually increased until the connectedcomponents—in this case, regions or volumes—formed thereby stair-step oroverrun seed points for other structures.

An advantage of 3D segmentation (used alone or in connection with 2Dsegmentation) is that it can take advantage of structures that are morereadily identifiable in 3D (i.e., across multiple slices) than inindividual 2D slices. For example, in the abdominal and lung region,blood vessels like the aorta may have diameters larger than some of thethinner bone structures. While the procedures discussed in the precedingsection might therefore result in the aorta being marked as vasculature,and vice versa, use of 3D segmentation in accord herewith circumventsthe problem.

Therefore in the illustrated embodiment the above described 2D method isextended as follows using a three dimensional approach: Seed points areplaced in voxels which are known to be either bone or vessel. Then fromthese seed points a flood fill is started to mark connected voxelsbelonging to the same structure. Measures are taken to avoid the floodfill from “spilling over” from vessel into bone structures or viceversa.

In the example this is implemented as follows: Anatomic structures inthe pelvic and abdomen region are searched which can be easily foundwith model based detection methods. See, step 76. For example the aortais a tubular structure with a known diameter range, always rathercentered and always located in front of the spine structure. Also thepelvis bone can be located by searching in the pelvic region for thelargest 3D-connected component.

Once those components are identified, seed points can be placed intothese objects. See, step 78.

Then from these seed-points a flood-fill algorithm is started. See, step80. The flood-fill first uses a very conservative intensity rangemeaning that only voxels with an intensity very similar to the seedpoints are marked. See step 82. Then, the range is grown to fill/markmore voxels belonging to the same anatomic structure. See, step 84. Ifthe range was made too wide, the flood-fill could erroneously spread outinto a spatially close structure. This is the reverse process of theabove described melting/separation problem for vessels running nearbones. For example with a seed point placed in the aorta, the flood fillcould spill over into the spine if the range was chosen such that thosetwo structures would meld. This must be avoided. To do so, step 86 isexecuted and a number of criteria are evaluated while growing theintensity range.

(i) If the volume filled by the flood fill stair steps, whencontinuously increasing the range, then it must be assumed that theflood-fill has spilled over and the range is reduced again. See, steps88 and 90.

(ii) If the flood fill reaches a voxel which is marked as a seed pointfor a different structure then also it must be assumed that the range istoo wide and the range is reduced again. See, steps 90 and 92. Forexample if a flood fill is started at a seedpoint in the aorta and itreaches a point marked bone in the pelvis, then the range was obviouslychosen such that the vessel tree and the bones are connected relative tothis range. Therefore this range must be rejected and narrowed.

Any voxels which are marked by the above method can be marked bone orvessel respectively, depending on which seed point they were connectedto (or grown from). The pixels correspond to such voxels do not need tobe re-labeled when this method is combined with the slice basedsegmentation described above.

Following segmentation of a data set of images 14 utilizingtwo-dimensional and/or three-dimensional segmentation in the mannerdiscussed above, the data set (and/or individual images 14 containedtherein) can be displayed on output device 28. Different renderingsettings can be set for the structures labelled by such segmentation,e.g., to facilitate visualization. For example, as shown on the rightside of FIG. 2 and discussed above, voxels labelled as bone can berendered invisibly, while voxels labelled as blood vessels can berendered in MIP mode. Of course, other display settings can be usedinstead, e.g., with blood vessels rendered in one color and bone inanother color, and so forth. Moreover, as noted above, the data set maybe subjected to still further processing on device 26, before, after, orin lieu of display. Alternatively, or in addition, it may transmittedelsewhere (e.g., to other equipment and/or locations) for storage,processing, and/or display.

The embodiments described herein meet the objects set forth above, amongothers. Thus, those skilled in the art will appreciate that theillustrated embodiment merely comprises an example of the invention andthat other embodiments making changes thereto fall within the scope ofthe invention, of which we claim:

1. A method for processing one or more two-dimensional (2D) image slicesof a three-dimensional (3D) volume comprising, for each of the one ormore images: A. identifying a region of the 3D volume to which the 2Dimage belongs; B. performing segmentation on the 2D image to identifyconnected components and labeling their pixels based on geometriccharacteristics of those components; C. where such labeling is based ona volumetric region to which the 2D image belongs.
 2. The method ofclaim 1, wherein step (A) comprises computing a histogram of the 2Dimage and comparing it with one or more other histograms.
 3. The methodof claim 2, wherein step (A) comprises assigning, as the region of the3D volume to which the 2D image belongs, a region associated with a saidhistogram to which the histogram of the 2D image best compares.
 4. Themethod of claim 4, wherein step (A) comprises responding toinconsistency in assignment of a plurality of 2D images to regions byre-assigning one or more 2D images to another region.
 5. The method ofclaim 2, wherein step (A) includes determining additional informationabout the 2D image by any of (i) performing a threshold segmentation onthe 2D image and determining therefrom any of (A) a number of connectedcomponents in the resulting image and/or (B) a total area occupied byselected structures and/or types of structures, (ii) computing ahistogram of any of said selected structures and/or types of structures.6. The method of claim 5, wherein step (A) comprises using theadditional information to determine a volumetric region to which the 2Dimage belongs.
 7. The method of claim 1, wherein the 2D images aregenerated via computed tomography.
 8. The method of claim 1, wherein theregion is a body region.
 9. The method of claim 8, wherein step (A)comprises (i) computing a histogram of the 2D image and comparing itwith one or more other histograms, (ii) determining additionalinformation about the 2D image by any of performing a thresholdsegmentation on the 2D image and determining therefrom any of (a) anumber of connected components in the resulting image and/or (b) a totalarea occupied by selected structures and/or types of structures, wherethe threshold segmentation is performed at a threshold of about −1000HU, and wherein the selected structures and/or types of structures arebody tissues, and computing a histogram of any of said selectedstructures and/or types of structures, (iii) using the additionalinformation to determine a volumetric region to which the 2D imagebelongs.
 10. A method for processing one or more two-dimensional (2D)image slices of a three-dimensional (3D) volume comprising, for each ofthe one or more images: A. identifying regions of the 3D volume to whichthe 2D image belongs; B. performing segmentation on the 2D image toidentify connected components and labeling their pixels based ongeometric characteristics of those components in respect to a volumetricregion to which the 2D image belongs; and C. wherein step (B) comprisesperforming a threshold segmentation on the 2D image to label identicallypixels representing the structures of interest.
 11. The method of claim10, wherein step (B) comprises identifying connected components in the2D image following threshold segmentation and relabeling pixels asdiffering types of structures of interest based on geometric propertiesof those connected components.
 12. The method of claim 10, wherein thegeometric property is shape and/or size.
 13. The method of claim 10,wherein step (B) comprises performing segmentation of the 2D image withone or more increasingly larger thresholds to identify connectedcomponents that separate into multiple components as a consequence ofsuch threshold increase.
 14. The method of claim 13, wherein step (B)comprises relabeling as differing types of structures of interest pixelsmaking up connected components that separate into multiple components asa consequence of such threshold increase.
 15. The method of claim 10,wherein the 2D images are generated via computed tomography.
 16. Themethod of claim 10, wherein the regions are body regions.
 17. The methodof claim 10, wherein (i) the structures of interest are one or more ofblood vessels and bone, and (ii) the threshold segmentation is performedat a threshold of about 130 HU.
 18. The method of claim 17, wherein step(B) comprises performing segmentation of the 2D image with one or moreincreasingly larger thresholds to identify connected components thatseparate into multiple components as a consequence of such thresholdincrease, wherein the increasingly large thresholds increase by a stepof about 1 HU.
 19. A method for processing a plurality oftwo-dimensional (2D) image slices of a three-dimensional (3D) volumecomprising, for each of the one or more images: A. identifying regionsof the 3D volume to which each 2D image belongs; B. performingsegmentation on the 2D image to identify connected components andlabeling their pixels based on geometric characteristics of thosecomponents; C. performing segmentation on a 3D data set formed from theplurality of the 2D images to label voxels representing structures ofinterest.
 20. The method of claim 19, wherein step (C) comprisesflood-filling from one or more seed points placed at voxels in one ormore structures of interest.
 21. The method of claim 20, wherein step(C) comprises increasing a voxel intensity range for flood-filling untila connected component formed thereby overextends.
 22. The method ofclaim 20, wherein step (C) comprises increasing a voxel intensity rangefor flood-filling until a connected component formed thereby any ofstair-steps and overruns a seed point placed in a voxel representinganother structure of interest.
 23. The method of claim 19, wherein step(C) comprises using model-based detection to find structures of interestin the 3D data set.
 24. The method of claim 19, wherein pixelscorresponding to voxels labeled in step (C) are not relabeled in step(B).
 25. The method of claim 19, wherein the 2D images are generated viacomputed tomography, wherein the regions are body regions and whereinthe structures of interest are one or more of blood vessels and bone.26. A method for processing a plurality of two-dimensional (2D) imageslices of a three-dimensional (3D) volume comprising, for each of theone or more images: A. identifying regions of the 3D volume to which the2D image belongs; B. performing threshold segmentation on the 2D imageto identify connected components and labeling their pixels based ongeometric characteristics of those components and in respect to avolumetric region to which the 2D image belongs; and C. performingsegmentation on a 3D data set formed from the plurality of the 2D imagesto label voxels representing the structures of interest.
 27. The methodof claim 26, wherein step (A) comprises computing a histogram of the 2Dimage and comparing it with one or more other histograms.
 28. The methodof claim 27, wherein step (A) comprises assigning, as the region of the3D volume to which the 2D image belongs, a region associated with a saidhistogram to which the histogram of the 2D image best compares.
 29. Themethod of claim 28, wherein step (A) comprises responding toinconsistency in assignment of a plurality of 2D images to regions byre-assigning one or more 2D images to another region.
 30. The method ofclaim 27, wherein step (A) includes determining additional informationabout the 2D image by any of (i) performing a threshold segmentation onthe 2D image and determining therefrom any of (a) a number of connectedcomponents in the resulting image and/or (b) a total area occupied byselected structures and/or types of structures, (ii) computing ahistogram of any of said selected structures and/or types of structures.31. The method of claim 30, wherein step (A) comprises using theadditional information to determine a volumetric region to which the 2Dimage belongs.
 32. The method of claim 26, wherein the 2D images aregenerated via computed tomography.
 33. The method of claim 26, whereinthe regions are body regions.
 34. The method of claim 33, wherein step(A) comprises (i) computing a histogram of the 2D image and comparing itwith one or more other histograms, (ii) determining additionalinformation about the 2D image by any of performing a thresholdsegmentation on the 2D image and determining therefrom any of (a) anumber of connected components in the resulting image and/or (b) a totalarea occupied by selected structures and/or types of structures, wherethe threshold segmentation is performed at a threshold of about −1000HU, and wherein the selected structures and/or types of structures arebody tissues, and computing a histogram of any of said selectedstructures and/or types of structures, (iii) using the additionalinformation to determine a volumetric region to which the 2D imagebelongs.
 35. The method of claim 26, wherein step (B) comprisesidentifying connected components in the 2D image following thresholdsegmentation and relabeling pixels as differing types of structures ofinterest based on geometric properties of those connected components.36. The method of claim 26, wherein the geometric property is shapeand/or size.
 37. The method of claim 26, wherein step (B) comprisesperforming segmentation of the 2D image with one or more increasinglylarger thresholds to identify connected components that separate intomultiple components as a consequence of such threshold increase.
 38. Themethod of claim 37, wherein step (B) comprises relabeling as differingtypes of structures of interest pixels making up connected componentsthat separate into multiple components as a consequence of suchthreshold increase.
 39. The method of claim 26, wherein the 2D imagesare generated via computed tomography.
 40. The method of claim 26,wherein the regions are body regions.
 41. The method of claim 26,wherein (i) the structures of interest are one or more of blood vesselsand bone, and (ii) the threshold segmentation is performed at athreshold of about 130 HU.
 42. The method of claim 41, wherein step (B)comprises performing segmentation of the 2D image with one or moreincreasingly larger thresholds to identify connected components thatseparate into multiple components as a consequence of such thresholdincrease, wherein the increasingly large thresholds increase by a stepof about 1 HU.
 43. The method of claim 26, wherein step (C) comprisesflood-filling from one or more seed points placed at voxels in one ormore structures of interest.
 44. The method of claim 43, wherein step(C) comprises increasing a voxel intensity range for flood-filling untila connected component formed thereby overextends.
 45. The method ofclaim 43, wherein step (C) comprises increasing a voxel intensity rangefor flood-filling until a connected component formed thereby any ofstair-steps and overruns a seed points placed in a voxel representinganother structure of interest.
 46. The method of claim 26, wherein step(C) comprises using model-based detection to find structures of interestin the 3D data set.
 47. The method of claim 26, wherein pixelscorresponding to voxels labeled in step (C) are not relabeled in step(B).
 48. The method of claim 26, wherein the 2D images are generated viacomputed tomography, wherein the regions are body regions and whereinthe structures of interest are one or more of blood vessels and bone.